Production scheduling under demand uncertainty in the presence of feedback: Model comparisons, insights, and paradoxes

Venkatachalam Avadiappan, Dhruv Gupta, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We investigate the importance of accounting for uncertainty a priori in production scheduling in the presence of feedback. First, we examine different optimization models (deterministic, robust, and stochastic programming), used to generate the open-loop schedules and describe the modeling of uncertainty in each case. Second, we present a formal procedure for carrying out closed-loop simulations in order to study and compare the closed-loop performance across the models as attributes such as the demand uncertainty observation horizon, order size max-mean relative difference, and load on the process network are varied. Finally, we analyze the results of the simulations to draw insights on how the above attributes affect the closed-loop performance of the different models across networks and expound on the paradoxes observed.

Original languageEnglish (US)
Article number108028
JournalComputers and Chemical Engineering
Volume168
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications

Keywords

  • Mixed-integer programming
  • Online scheduling
  • Real-time optimization

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